1. Field of the Invention
The present invention relates to a depth image-based modeling method and apparatus, and more particularly, to a depth image-based modeling method and apparatus which obtain depth and color information from a polygonal model.
2. Description of Related Art
Depth image-based representation (DIBR) is a technique of synthesizing a plurality of images obtained from respective virtual points of an object rendered in a still or moving image and pixel-wise depth information of the images. In general, DIBR is divided into the following two processes: re-projecting a plurality of points of an original image to a three-dimensional (3D) space using depth information of each pixel of the original image; and projecting the re-projection results onto an image plane of a virtual camera located at a given viewing location. In other words, DIBR includes the re-projection of a two-dimensional (2D) image to a 3D world and the projection of the re-projection result back to a 2D space.
FIGS. 1A through 1C are diagrams illustrating a conventional DIBR modeling method. Referring to FIG. 1A, in the conventional DIBR modeling method, a plurality of virtual cameras 11 are placed at a plurality of locations near the object 10, thereby obtaining a plurality of images of an object 10. FIG. 1B illustrates a plurality of color images 12 and a plurality of depth images 13 of the object 10 of FIG. 1A obtained by the virtual cameras 11. The depth images 13 are gray scale images having depth information. The color images 12 and the depth images 13 of the object 10 are taken by the virtual cameras 11, which are respectively located on the left, right, upper, and lower sides of the object 10, as illustrated in FIG. 1A. FIG. 1C illustrates a DIBR image obtained by synthesizing the color images 12 and the depth images 13 of FIG. 1B.
The conventional DIBR modeling method, however, requires a plurality of virtual cameras to render an object, and thus results in a low rendering speed. This problem becomes more apparent when rendering of the object is complicated because the more complicated the rendering, the more virtual cameras are required.
In addition, in the conventional DIBR modeling method, a camera bounding volume (BV), which is a minimum volume that can surround an object as illustrated in FIGS. 2A and 2B, must be manually adjusted. Thus, a user's capability to manually adjust the camera BV has a considerable effect on the quality of a DIBR image. For example, FIG. 2A illustrates an example of a well-optimized BV, and FIG. 2B illustrates an example of a poorly optimized BV.
Moreover, in the conventional DIBR modeling method, a virtual camera range needs to be set by a user according to the complexity of an object to be rendered.